Abstract Reflectance capture aims at the visual reproduction of an object under varying illumination. Past works differ substantially in their experimental overhead, from single‐ or few‐image approaches, that employ significant (often learned) priors at the expense of biased reconstructions, to more accurate approaches that tend to be time‐consuming, which to a good part is due to the need for carefully controlled illumination. Moreover, as we will show, the frequently employed point‐light or directional lighting tends to clip highlights and under‐sample the reflectance of glossy surfaces, leading to incorrect reconstructions under previously unseen illumination. Our work aims to strike a new balance, combining a low‐overhead capture methodology with a fast (neural) model fit. A key feature of our approach is the use of handheld, indirect bounce light that enables a convenient capture methodology, limits the dynamic range of the reflectance (effectively avoiding highlight clipping) and ensures contiguous hemispherical incidence, even with few images, eliminating under‐sampling of highly specular reflectance lobes. Moreover, our approach does not require training on pre‐existing material datasets and thus is not restricted by the choice of dataset, and its inference scales linearly with the number of pixels, scaling exceptionally well to large image sizes. As a result, our method enables high‐resolution capture of a spatially‐varying reflectance distribution function (SVBRDF) from a small set of casually captured, indirectly lit photographs, making high‐quality material acquisition practical even on consumer hardware. Overall, we believe that our method occupies a unique trade‐off between acquisition effort, model assumptions and resulting quality, and it has the potential to transform areas that routinely use handheld point‐light sources, such as the popular reflectance transformation imaging (RTI), leading to more faithful reproductions of artefacts and their surface characteristics.

We present a novel method for accurately calibrating the optical properties of full-color 3D printers using only a single, directly printable calibration target. Our approach is based on accurate multiple-scattering light transport and estimates the single-scattering albedo and extinction coefficient for each resin. These parameters are essential for both soft-proof rendering of 3D printouts and for advanced, scattering-aware 3D halftoning algorithms. In contrast to previous methods that rely on thin, precisely fabricated resin samples and labor-intensive manual processing, our technique achieves higher accuracy with significantly less effort. Our calibration target is specifically designed to enable algorithmic recovery of each resin's optical properties through a series of one-dimensional and two-dimensional numerical optimizations, applied first on the white and black resins, and then on any remaining resins. The method supports both RGB and spectral calibration, depending on whether a camera or spectrometer is used to capture the calibration target. It also scales linearly with the number of resins, making it well-suited for modern multi-material printers. We validate our approach extensively, first on synthetic and then on real resins across 242 color mixtures, printed thin translucent samples, printed surface textures, and fully textured 3D models with complex geometry, including an eye model and a figurine.

Abstract Achieving photorealism when rendering virtual scenes in movies or architecture visualizations often depends on providing a realistic illumination and background. Typically, spherical environment maps serve both as a natural light source from the Sun and the sky, and as a background with clouds and a horizon. In practice, the input is either a static high‐resolution HDR photograph manually captured on location in real conditions, or an analytical clear sky model that is dynamic, but cannot model clouds. Our approach bridges these two limited paradigms: a user can control the sun position and cloud coverage ratio, and generate a realistically looking environment map for these conditions. It is a hybrid data‐driven analytical model based on a modified state‐of‐the‐art GAN architecture, which is trained on matching pairs of physically‐accurate clear sky radiance and HDR fisheye photographs of clouds. We demonstrate our results on renders of outdoor scenes under varying time, date and cloud covers. Our source code and a dataset of 39 000 HDR sky images are publicly available at https://github.com/CGGMFF/SkyGAN .

We present a spectral measurement approach for the bulk optical properties of translucent materials using only low-cost components. We focus on the translucent inks used in full-color 3D printing, and develop a technique with a high spectral resolution, which is important for accurate color reproduction. We enable this by developing a new acquisition technique for the three unknown material parameters, namely, the absorption and scattering coefficients, and its phase function anisotropy factor, that only requires three point measurements with a spectrometer. In essence, our technique is based on us finding a three-dimensional appearance map , computed using Monte Carlo rendering, that allows the conversion between the three observables and the material parameters. Our measurement setup works without laboratory equipment or expensive optical components. We validate our results on a 3D printed color checker with various ink combinations. Our work paves a path for more accurate appearance modeling and fabrication even for low-budget environments or affordable embedding into other devices.

In full-color inkjet 3D printing, a key problem is determining the material configuration for the millions of voxels that a printed object is made of. The goal is a configuration that minimises the difference between desired target appearance and the result of the printing process. So far, the techniques used to find such a configuration have relied on domain-specific methods or heuristic optimization, which allowed only a limited level of control over the resulting appearance. We propose to use differentiable volume rendering in a continuous material-mixture space, which leads to a framework that can be used as a general tool for optimising inkjet 3D printouts. We demonstrate the technical feasibility of this approach, and use it to attain fine control over the fabricated appearance, and high levels of faithfulness to the specified target.

We present a fitted model of sky dome radiance and attenuation for realistic terrestrial atmospheres. Using scatterer distribution data from atmospheric measurement data, our model considerably improves on the visual realism of existing analytical clear sky models, as well as of interactive methods that are based on approximating atmospheric light transport. We also provide features not found in fitted models so far: radiance patterns for post-sunset conditions, in-scattered radiance and attenuation values for finite viewing distances, an observer altitude resolved model that includes downward-looking viewing directions, as well as polarisation information. We introduce a fully spherical model for in-scattered radiance that replaces the family of hemispherical functions originally introduced by Perez et al., and which was extended for several subsequent analytical models: our model relies on reference image compression via tensor decomposition instead.